Modeling Unobserved Heterogeneity in New York Dairy Farms: One-Stage versus Two-Stage Models

Author:

Alvarez Antonio,del Corral Julio,Tauer Loren W.

Abstract

Agricultural production estimates have often differentiated and estimated different technologies within a sample of farms. The common approach is to use observable farm characteristics to split the sample into groups and subsequently estimate different functions for each group. Alternatively, unique technologies can be determined by econometric procedures such as latent class models. This paper compares the results of a latent class model with the use of a priori information to split the sample using dairy farm data. Latent class separation appears to be a superior method of separating heterogeneous technologies and suggests that technology differences are multifaceted.

Publisher

Cambridge University Press (CUP)

Subject

Economics and Econometrics,Agronomy and Crop Science

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3. The statistics can be written as: AIC = −2 · log LF(J) + 2 · m; SBIC = −2 · log LF(J) + log(n) · m, where LF(J) is the value that the likelihood function takes for J groups, m is the number of parameters used in the model, and n is the number of observations. The preferred model will be that for which the value of the statistic is lowest.

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